A BIM-based wharf berth structure safety monitoring and early warning method and system

Abstract

The present application relates to the wharf berth monitoring technical field, specifically relates to a kind of wharf berth structure safety monitoring early warning method and system based on BIM, comprising: the BIM model of wharf berth structure is built, and the physical parameter in the structure operation process is collected by sensor network;The collected physical parameter is subjected to multi-factor coupling analysis to determine structure modal coupling effect;Based on the modal coupling effect, the dynamic safety margin of structure is calculated, and the adaptive early warning threshold is generated according to the dynamic safety margin;When the structure parameter monitored exceeds the adaptive early warning threshold, early warning signal is sent;The present application can realize the accurate evaluation of wharf berth structure dynamic risk, and provide reliable guarantee for its whole life cycle safety.

Description

Technical Field

[0001] This invention relates to the field of wharf berth monitoring technology, specifically to a BIM-based method and system for monitoring and early warning of structural safety of wharf berths. Background Technology

[0002] In the field of safety monitoring of wharf berth structures, existing technologies generally adopt a monitoring mode based on a single physical quantity. This involves collecting local structural parameters using equipment such as strain gauges and displacement sensors, and issuing safety warnings based on fixed thresholds. Such methods only reflect the isolated state of the structure at a specific moment, failing to consider the temporal and spatial coupling effects of multiple factors such as ship impacts, tidal loads, temperature changes, and material corrosion. In actual working conditions, the superposition of high-frequency ship impacts and extreme temperatures can significantly amplify the structural vibration response, and tidal fluctuations can exacerbate the synergistic effect of chloride ion penetration and steel corrosion. These implicit multi-factor interactions are often key causes of progressive structural deterioration. Because existing technologies lack the ability to quantify these coupling effects, static thresholds are difficult to adapt to the dynamic risk evolution process, often resulting in delayed warnings or false alarms, thus failing to provide accurate full life-cycle safety assurance for wharf berth structures.

[0003] Based on the above problems, there is an urgent need for a monitoring and early warning technology solution that can comprehensively address factors such as high-frequency impacts and extreme temperatures of ships. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a BIM-based method for structural safety monitoring and early warning of wharf berths, comprising:

[0005] Construct a BIM model of the wharf berth structure and collect physical parameters of the structure during operation through a sensor network;

[0006] Multi-factor coupling analysis was performed on the collected physical parameters to determine the structural modal coupling effect;

[0007] The dynamic safety margin of the structure is calculated based on the modal coupling effect, and an adaptive early warning threshold is generated based on the dynamic safety margin.

[0008] An early warning signal is issued when the monitored structural parameters exceed the adaptive early warning threshold.

[0009] Preferably, the sensor network includes fiber optic strain sensors, triaxial accelerometers, temperature and humidity sensors, and water level sensors. The fiber optic strain sensors are deployed at key stress nodes of the wharf berth structure to collect strain data. The triaxial accelerometers are installed at vibration-sensitive parts of the structure to collect vibration acceleration data. The temperature and humidity sensors are deployed on the structural surface and surrounding environment to collect temperature and humidity data. The water level sensors are set in the water-adjacent area of ​​the wharf berth to collect water level data. All data collected by the sensors are uploaded to the data processing center in real time via wired transmission.

[0010] More preferably, the multi-factor coupling analysis includes obtaining the ship impact force frequency, structural natural frequency, temperature change, reference temperature, water level density change, freshwater density benchmark, creep stress increment, and material yield strength, and substituting the above parameters into a preset calculation model to obtain the modal coupling coefficient. The modal coupling coefficient is used to quantify the synergistic effect between ship impact, tidal load, and temperature gradient.

[0011] More preferably, the dynamic safety margin calculation includes obtaining the structural ultimate strength, equivalent stress, environmental acceleration factor, plastic strain rate and corrosion stress increment, and combining the modal coupling coefficient to obtain the dynamic safety margin index through integration. The dynamic safety margin index changes dynamically over time to reflect the hidden degradation path of the structure.

[0012] More preferably, the modal coupling coefficient is calculated using the following formula:

[0013] ;

[0014] in, For modal coupling coefficients, , The weighting coefficients were determined through orthogonal experiments. This refers to the change in temperature. For reference temperature; The frequency of the ship's impact force; The natural frequency of the structure; This refers to changes in water level density. Used as a freshwater density benchmark; This represents the creep stress increment; This represents the yield strength of the material.

[0015] More preferably, the dynamic safety margin index is calculated using the following formula:

[0016] ;

[0017] in, For dynamic safety margin index, The ultimate strength of the structure; Equivalent stress; This is the environmental acceleration factor, which is related to humidity and chloride ion concentration. Plastic strain rate; This represents the increase in corrosion stress. The modal coupling coefficient; For time.

[0018] More preferably, the adaptive early warning threshold is generated by the following formula:

[0019] ;

[0020] in, For adaptive early warning threshold, For static design thresholds; This is the coupling sensitivity coefficient; The modal coupling coefficient; This is an increase in load beyond the design capacity; For design loads; This represents the vibration frequency offset. This is the critical resonance frequency.

[0021] A BIM-based safety monitoring and early warning system for wharf berth structures, applied to a BIM-based safety monitoring and early warning method for wharf berth structures as described in any of the above-mentioned methods, includes a BIM model building module and a sensor data acquisition module. The BIM model building module is used to construct a three-dimensional BIM model of the wharf berth structure, and the sensor data acquisition module is used to collect physical parameters during the structure's operation. The system is characterized by further including a multi-factor coupling analysis module, a dynamic safety margin assessment module, an adaptive early warning threshold generation module, and an early warning module. The multi-factor coupling analysis module is connected to the sensor data acquisition module and is used to receive physical parameters and perform multi-factor coupling analysis to determine the structural modal coupling effect. The dynamic safety margin assessment module is connected to the multi-factor coupling analysis module and is used to calculate the dynamic safety margin of the structure based on the modal coupling effect. The adaptive early warning threshold generation module is connected to both the dynamic safety margin assessment module and the BIM model building module and is used to generate an adaptive early warning threshold based on the dynamic safety margin. The early warning module is connected to the adaptive early warning threshold generation module and is used to issue an early warning signal when the monitored structural parameters exceed the adaptive early warning threshold.

[0022] More preferably, the sensor data acquisition module includes a fiber optic strain sensing unit, a triaxial acceleration sensing unit, a temperature and humidity sensing unit, a water level sensing unit, and a data transmission unit; the output terminals of the fiber optic strain sensing unit, the triaxial acceleration sensing unit, the temperature and humidity sensing unit, and the water level sensing unit are respectively connected to the input terminal of the data transmission unit; the output terminal of the data transmission unit is connected to the input terminal of the multi-factor coupling analysis module; the data transmission unit uses industrial Ethernet for data transmission and supports data encryption and breakpoint resume functionality.

[0023] Further preferably, the multi-factor coupling analysis module includes a parameter extraction unit and a coupling coefficient calculation unit; the input end of the parameter extraction unit is connected to the output end of the sensor data acquisition module, and is used to extract parameters such as ship impact force frequency, structural natural frequency, and temperature change from the acquired physical parameters; the input end of the coupling coefficient calculation unit is connected to the output end of the parameter extraction unit, and is used to calculate the modal coupling coefficient based on the extracted parameters; the adaptive early warning threshold generation module includes a threshold benchmark acquisition unit and a threshold adjustment unit; the threshold benchmark acquisition unit is connected to the BIM model construction module, and is used to acquire the static design threshold from the BIM model; the threshold adjustment unit is connected to both the threshold benchmark acquisition unit and the multi-factor coupling analysis module, and is used to adjust the static design threshold in conjunction with the modal coupling coefficient to generate an adaptive early warning threshold.

[0024] Technical effects:

[0025] This invention quantifies the synergistic effects of factors such as ship impact, tides, and temperature through multi-factor coupling analysis. Based on the coupling effect, it calculates a dynamic safety margin to track the hidden deterioration path of the structure, and generates an adaptive early warning threshold by combining the margin. This innovative technology overcomes the limitations of existing single-parameter monitoring and static thresholds, and solves the problem of delayed early warning or false alarms caused by the neglect of multi-factor coupling effects in the background technology. It enables accurate assessment of the dynamic risks of wharf berth structures and provides reliable protection for their safety throughout their entire life cycle. Attached Figure Description

[0026] Figure 1 This is a flowchart of the BIM-based method for monitoring and early warning of structural safety at wharf berths, as described in this application.

[0027] Figure 2 This is a block diagram of the BIM-based safety monitoring and early warning system for wharf berth structures in this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] Traditional technical solutions have the following technical problems: existing wharf berth structure monitoring mostly relies on a single physical quantity, such as strain or displacement, for independent analysis, and uses static thresholds as early warning benchmarks. They do not consider the coupling effects of multiple factors such as ship impact, tidal load, and temperature changes in time and space, making it difficult to capture the hidden deterioration path of the structure, resulting in delayed early warnings or frequent false alarms, and failing to meet the dynamic safety assessment needs in complex marine environments.

[0030] Based on this, please refer to Figure 1 This embodiment provides a BIM-based method for monitoring and early warning of the safety of wharf berth structures. The method includes constructing a BIM model of the wharf berth structure, collecting physical parameters during structural operation via a sensor network, performing multi-factor coupling analysis on the collected physical parameters to determine the structural modal coupling effect, calculating the dynamic safety margin of the structure based on the modal coupling effect, generating an adaptive early warning threshold based on the dynamic safety margin, and issuing an early warning signal when the monitored structural parameters exceed the adaptive early warning threshold. This scheme overcomes the limitations of traditional single-parameter monitoring and static thresholds by constructing a closed-loop logic of multi-factor coupling analysis, dynamic safety margin assessment, and adaptive threshold early warning. The multi-factor coupling analysis integrates the cross-influence of factors such as ships, tides, and temperature; the dynamic safety margin is updated with the cumulative effect over time; and the adaptive threshold is correlated with real-time operating conditions, realizing a shift from passive response to proactive prediction.

[0031] The technological benefits are reflected in its ability to accurately identify structural risks under the combined effect of multiple factors, capture hidden signs of deterioration in advance, avoid early warning failures caused by misjudgment of a single parameter or rigidity of static thresholds, and provide dynamic safety assurance for the entire life cycle of wharf berth structures. It is especially suitable for long-term monitoring in complex marine environments such as high salt spray and strong temperature differences.

[0032] Traditional technical solutions have the following technical problems: existing monitoring systems use a single type of sensor and are deployed in a crude manner, often using a single-point layout. Data transmission relies on wireless methods and is susceptible to electromagnetic interference, resulting in incomplete physical parameters and insufficient spatial resolution. The strain changes and vibration characteristics of key stress points cannot be accurately captured, affecting the accuracy of subsequent multi-factor analysis.

[0033] Based on this, the sensor network includes fiber optic strain sensors, triaxial accelerometers, temperature and humidity sensors, and water level sensors. Fiber optic strain sensors are deployed at key stress nodes of the wharf berth structure to collect strain data. Triaxial accelerometers are installed at vibration-sensitive parts of the structure to collect vibration acceleration data. Temperature and humidity sensors are deployed on the structural surface and surrounding environment to collect temperature and humidity data. Water level sensors are set in the water-adjacent area of ​​the wharf berth to collect water level data. All data collected by the sensors are uploaded to the data processing center in real time via wired transmission.

[0034] This solution constructs a comprehensive data acquisition system through the targeted deployment of multiple types of sensors and the guarantee of wired transmission. Fiber optic grating sensors monitor strain at key stress points, accelerometers dynamically capture vibration-sensitive parts, and environmental sensors record temperature, humidity, and water level in real time, forming a multi-dimensional data matrix. Wired transmission avoids data loss or delay caused by wireless interference. The technical benefits lie in ensuring the integrity and accuracy of the acquired data, providing high-quality input for multi-factor coupling analysis. Strain data at key stress points accurately reflects the distribution of internal forces in the structure, acceleration data from vibration-sensitive parts captures the dynamic response of ship impacts, environmental parameters provide an environmental benchmark for coupling effect analysis, and wired transmission ensures data real-time performance, laying a reliable data foundation for subsequent safety assessments.

[0035] Traditional technical solutions have the following technical problems: existing structural safety assessments often analyze factors such as ship impact, tidal load, and temperature changes independently, without considering the synergistic effect between these factors. This leads to insufficient understanding of the structural modal coupling effect and an inability to quantify the nonlinear risks caused by the superposition of multiple factors. For example, the phenomenon that high-frequency ship impacts in high-temperature environments may exacerbate structural fatigue damage is overlooked, affecting the comprehensiveness of the safety assessment.

[0036] Based on this, the multi-factor coupling analysis includes obtaining the ship impact force frequency, structural natural frequency, temperature change, reference temperature, water level density change, freshwater density benchmark, creep stress increment and material yield strength. The above parameters are substituted into a preset calculation model to obtain the modal coupling coefficient. The modal coupling coefficient is used to quantify the synergistic effect between ship impact, tidal load and temperature gradient.

[0037] This scheme extracts key parameters from multiple dimensions and constructs a coupling coefficient calculation model to achieve quantitative analysis of the interactions of multiple factors under complex working conditions. The ratio of the ship impact force frequency to the structure's natural frequency reflects the resonance risk; the ratio of temperature change to reference temperature reflects the impact of temperature stress; and changes in water level density and creep stress increments are related to tidal loads and long-term material degradation. These parameters are integrated through the model to form coupling coefficients. The technical effect is reflected in the ability to accurately identify the risk amplification effect under the synergistic effect of multiple factors. For example, when high-temperature environments reduce the yield strength of materials, the superimposed effect of ship impact and tidal loads can be quantified through coupling coefficients, avoiding the underestimation of risk caused by single-factor analysis. This provides a scientific benchmark for subsequent dynamic safety margin calculations, improving the systematicness and accuracy of structural safety assessments.

[0038] Traditional technical solutions have the following technical problems: existing structural safety margin assessments mostly use fixed safety factors, which do not consider the cumulative effect of deterioration over time and cannot reflect the evolution of latent damage such as corrosion and fatigue over time. This leads to deviations between the safety margin calculation and the actual structural state. For example, the accumulation of long-term corrosion and plastic deformation may make the actual load-bearing capacity of the structure much lower than the static assessment results.

[0039] Based on this, the dynamic safety margin calculation includes obtaining the structural ultimate strength, equivalent stress, environmental acceleration factor, plastic strain rate and corrosion stress increment, and combining the modal coupling coefficient to obtain the dynamic safety margin index through integral calculation. The dynamic safety margin index changes dynamically over time to reflect the hidden degradation path of the structure.

[0040] This scheme integrates static mechanical parameters with dynamic degradation factors to construct a safety margin assessment model that evolves over time. The ratio of structural ultimate strength to equivalent stress forms the basic safety benchmark, the environmental acceleration factor relates to environmental effects such as corrosion, the plastic strain rate and corrosion stress increment reflect the material degradation rate, and the modal coupling coefficient introduces the synergistic effect of multiple factors. Through integral calculations, the long-term degradation process can be dynamically tracked.

[0041] Traditional technical solutions have the following technical problems: existing technologies lack quantitative methods for the coupling effects of multiple factors such as ship impact, tidal load, and temperature gradient. They mostly rely on empirical judgment or single parameter weighting to achieve approximate assessments, which cannot accurately describe the nonlinear interaction between factors. This leads to the overestimation or underestimation of the impact of coupling effects. For example, the amplification effect of temperature changes on ship impact response cannot be accurately quantified, affecting the scientific nature of structural risk assessment.

[0042] Based on this, the modal coupling coefficient is calculated using the following formula:

[0043] ;

[0044] in, , The weighting coefficients were determined through orthogonal experiments. This refers to the change in temperature. For reference temperature; The frequency of the ship's impact force; The natural frequency of the structure; This refers to changes in water level density. Used as a freshwater density benchmark; This represents the creep stress increment; The formula represents the material's yield strength. It constructs a quantitative model of multi-factor coupling effects through piecewise multiplication and weighted summation. The first part integrates the frequency characteristics of temperature changes and ship impacts, reflecting the influence of temperature stress on the structural resonance response; the second part correlates water level density changes and material creep effects, demonstrating the synergistic effect of tidal loads and long-term deformation; the weighting coefficients are calibrated through orthogonal experiments to ensure the rationality of the contribution of each factor.

[0045] This formula quantifies the coupling effects among factors such as ship impact, tidal load, and temperature changes, addressing the problem that existing technologies cannot scientifically assess the synergistic effects of multiple factors. The formula employs a piecewise weighted summation structure, using two product terms to describe the interaction mechanisms of different physical fields, with weighting coefficients balancing the contribution of each coupling mechanism.

[0046] The first part is the coupling term between temperature and ship impact. This includes the ratio of the temperature change to the reference temperature. The effect of temperature stress on structural stiffness is reflected in the following: high temperatures reduce the elastic modulus of materials, making the structure more susceptible to deformation under external forces; low temperatures may increase the brittleness of materials and amplify impact damage.

[0047] The ratio of the ship's impact force frequency to its structural natural frequency This reflects the risk of resonance: when the two ratios are close, the structural vibration response will be significantly enhanced, accelerating fatigue damage. The product of these two ratios, then weighted by a coefficient... Adjustments can precisely quantify the short-term dynamic impact of the combined effects of temperature and impact on the structure.

[0048] The second part is the coupling term between tides and material creep. It represents the ratio of water level density change to a freshwater density baseline. Characterizing the unique nature of tidal loads: Seawater contains a high concentration of ions, and its density changes not only affect buoyancy loads but also accelerate concrete carbonation and steel corrosion.

[0049] The ratio of creep stress increment to material yield strength This reflects material degradation under long-term loading: creep leads to stress redistribution in the structure, while tidal cyclic loading exacerbates this process, causing latent damage to accumulate. The product of the two is weighted by a coefficient. Adjustments can quantify the combined effects of tidal and creep on the long-term durability of structures.

[0050] This design transforms previously isolated physical parameters into quantifiable coupling coefficients, reflecting the interaction mechanisms of different factors and adapting to the characteristics of different wharf structures through weighting coefficients. For example, it addresses the difference in coupling effects between high-pile wharves and gravity-type wharves, providing a scientific quantitative basis for subsequent safety assessments.

[0051] The technological advantage lies in achieving precise quantification of the coupling effects of multiple factors, such as under high-temperature environments. The frequency of ship impacts increases, approaching the structure's natural frequency. As the coupling coefficient approaches 1, The increase is significant, which directly reflects the risk amplification effect and provides a reliable quantitative basis for subsequent dynamic safety margin calculations. It overcomes the subjectivity and limitations of traditional experience-based assessments and improves the accuracy and repeatability of structural risk analysis.

[0052] Traditional technical solutions have the following technical problems: existing structural safety margin assessment methods mostly use fixed values ​​or simple linear decay models, which do not associate the coupling effect of multiple factors with the deterioration accumulation process over time. They cannot quantify the co-evolution law of latent damage such as corrosion, plastic deformation, and modal coupling, resulting in a large deviation between the safety margin calculation results and the actual load-bearing capacity of the structure, making it difficult to support the needs of long-term dynamic safety assessment.

[0053] Based on this, the dynamic safety margin index is calculated using the following formula:

[0054] ;

[0055] in, The ultimate strength of the structure; Equivalent stress of the structure; This is the environmental acceleration factor, which is related to humidity and chloride ion concentration. Plastic strain rate; This represents the increase in corrosion stress. The modal coupling coefficient; For time.

[0056] This scheme constructs a time evolution model of dynamic safety margin by introducing an exponential function and an integral term. (Basic terms) The ratio reflecting the static bearing capacity of the structure, while the exponential term is accumulated through integral calculation to account for the modal coupling effect. Plastic strain rate With corrosion stress increment Synergistic effect, environmental acceleration coefficient Further strengthen the weighting of environmental factors such as humidity and chloride ions.

[0057] This formula is used to dynamically assess the safety reserve of a structure, addressing the problem that traditional fixed safety factors cannot reflect the damage accumulation process. The formula combines a basic ratio term with an exponential decay term to achieve a time-series description of the structural safety state.

[0058] The fundamental term is the ratio of the structural ultimate strength to the equivalent stress. The ratio reflects the immediate load-bearing capacity of the structure: ultimate strength is the maximum load-bearing limit of a material or component, while equivalent stress is the combined stress state under current load and environmental influences. This ratio directly reflects the initial safety reserve of the structure; the larger the ratio, the higher the immediate safety.

[0059] The exponential decay term is the core innovation of the formula, capturing the cumulative effect of damage through integration. The integral variable is time. The integrand consists of three parts: modal coupling coefficients. This reflects the amplification effect of damage caused by the synergistic effect of multiple factors; the square root of the plastic strain rate and the increase in corrosion stress. The synergistic mechanism reflecting material degradation—plastic strain rate characterizes the rate of irreversible deformation of a structure due to repeated loading, while corrosion stress increment reflects the additional stress caused by steel corrosion and concrete deterioration. The square root of their product avoids dimensional mismatch and reinforces the interactive effect of plastic deformation accelerating corrosion and corrosion exacerbating plastic damage; environmental acceleration coefficient. Adjustments are made based on environmental parameters such as humidity and chloride ion concentration in highly corrosive environments. Increased size accelerates damage accumulation.

[0060] The introduction of the exponential function makes the damage accumulation exhibit non-linear characteristics, which is more in line with the actual deterioration law of the structure: when the initial damage is small, the safety margin decreases slowly; when the damage accumulates to a certain extent, such as when the corrosion stress exceeds a certain threshold, the safety margin will decay rapidly due to the exponential effect.

[0061] The technological benefits are reflected in its ability to accurately capture the entire process of a structure from a healthy state to latent degradation, such as the increase in corrosion stress after long-term exposure to a high-salt-spray environment. Increased, and simultaneously the coupling effect of ship impact and tidal load. The continuous effect of the integral term leads to an increase in the safety margin index. Nonlinear descent intuitively reflects the dynamic process of damage accumulation, providing a scientific quantitative basis for subsequent adjustment of warning thresholds. It overcomes the shortcomings of traditional static assessment methods that do not adequately consider the degradation over time, and improves the dynamism and accuracy of safety assessment.

[0062] Traditional technical solutions have the following technical problems: existing early warning thresholds are mostly based on static design standards and set fixed values, without being associated with the structural stress state and environmental conditions under real-time operating conditions. Under special operating conditions such as ship overload, extreme temperature, and resonance risk, fixed thresholds are prone to false alarms (thresholds are too low) or missed alarms (thresholds are too high), and cannot meet the dynamic early warning needs in complex environments.

[0063] Based on this, the adaptive warning threshold is generated using the following formula:

[0064] ;

[0065] in, For static design thresholds; This is the coupling sensitivity coefficient; The modal coupling coefficient; This is an increase in load beyond the design capacity; For design loads; This represents the vibration frequency offset. This is the critical resonance frequency.

[0066] This scheme uses a static design threshold as a benchmark and achieves real-time threshold adaptation by introducing a dynamic adjustment factor. The adjustment factor consists of the modal coupling coefficient. The ratio of over-design load Ratio of frequency offset Cooperative composition, coupling sensitivity coefficient This is used to balance the influence weights of various factors on the threshold. The technical effect is reflected in the ability to dynamically optimize the warning threshold based on real-time operating conditions, such as when ship overloading leads to an increase in load exceeding the design capacity. Increase, and at the same time, the vibration frequency shift Approaching the critical resonance frequency At that time, the adjustment factor increased significantly, and the adaptive early warning threshold... The corresponding increase avoids unnecessary false alarms triggered by instantaneous overload; and the modal coupling coefficient is improved accordingly in high-temperature environments. When the threshold is increased, it should be appropriately lowered to enhance the sensitivity of the early warning and ensure that hidden risks are captured.

[0067] This formula is used to generate warning thresholds that are dynamically adjusted according to operating conditions, solving the problem of false alarms or missed alarms caused by existing fixed thresholds under complex operating conditions. The formula uses the static design threshold as a benchmark and achieves real-time adaptation of the threshold through dynamic adjustment factors.

[0068] Static design threshold It is an initial safety boundary set based on specifications or historical data, reflecting the safety limits of the structure under normal operating conditions, and ensuring the rationality of the benchmark for threshold adjustment.

[0069] The dynamic adjustment factor is the key to the formula, and it consists of three parts working together: the coupling sensitivity coefficient. The weights used to balance the influence of dynamic factors are calibrated based on the structure's sensitivity to coupling effects, such as flexible wharves being more sensitive than rigid wharves; modal coupling coefficients. Introducing the real-time impact of multi-factor synergy, the stronger the coupling effect, the greater the threshold adjustment range; the square root of the overload ratio and frequency offset ratio. This quantifies immediate risk; overload ratio Frequency offset ratio reflects the degree to which the actual load exceeds the design value. It reflects the degree of proximity between the current vibration frequency and the critical resonance frequency. The square root of the sum of the two not only unifies the dimensions but also highlights the high-risk scenario of overload superimposed resonance.

[0070] Through this design, the threshold can be intelligently adjusted according to actual operating conditions: when the ship is overloaded and close to resonance, the adjustment factor increases, and the threshold is raised accordingly to avoid false alarms due to instantaneous load fluctuations; when coupling effects such as high temperature and high corrosion are significant, the adjustment factor is adjusted accordingly. This mechanism lowers the threshold, enhancing the sensitivity of early warning for latent damage. This dynamic mechanism makes the early warning more closely aligned with the actual structural risk, providing concrete support for the "adaptive early warning threshold generation" technical feature in the claims.

[0071] This dynamic adjustment mechanism makes the early warning system more closely aligned with actual working conditions, overcomes the rigidity of fixed thresholds, and improves the accuracy and adaptability of early warnings.

[0072] Traditional technical solutions have the following technical problems: existing monitoring systems are mostly composed of independent sensors, data processing and early warning modules. Data interaction between modules is not smooth, and there is a lack of a collaborative mechanism for multi-factor coupling analysis, dynamic safety margin assessment and adaptive early warning threshold generation. This results in a lag in the overall system response and makes it impossible to achieve end-to-end closed-loop management from data acquisition to risk early warning.

[0073] Based on this, please refer to Figure 2This embodiment provides a BIM-based safety monitoring and early warning system for wharf berth structures, comprising: a BIM model building module and a sensor data acquisition module. The BIM model building module is used to construct a three-dimensional BIM model of the wharf berth structure. The sensor data acquisition module is used to collect physical parameters during the structure's operation. The BIM-based wharf berth structure safety monitoring and early warning system includes a multi-factor coupling analysis module, a dynamic safety margin assessment module, an adaptive early warning threshold generation module, and an early warning module. The multi-factor coupling analysis module is connected to the sensor data acquisition module and is used to receive physical parameters and perform multi-factor coupling analysis to determine the structural modal coupling effect. The dynamic safety margin assessment module is connected to the multi-factor coupling analysis module and is used to calculate the dynamic safety margin of the structure based on the modal coupling effect. The adaptive early warning threshold generation module is connected to both the dynamic safety margin assessment module and the BIM model building module and is used to generate an adaptive early warning threshold based on the dynamic safety margin. The early warning module is connected to the adaptive early warning threshold generation module and is used to issue an early warning signal when the monitored structural parameters exceed the adaptive early warning threshold.

[0074] This solution achieves deep collaboration among modules by constructing a modular collaborative architecture encompassing data acquisition, coupled analysis, safety assessment, threshold generation, and early warning output. The multi-factor coupled analysis module transforms sensor data into quantitative results of modal coupling effects, the dynamic safety margin assessment module calculates the real-time safety status based on these results, the adaptive early warning threshold generation module combines the static parameters of the BIM model with the dynamic safety margin to generate an adaptive threshold, and finally, the early warning module executes the risk response.

[0075] The system's modules form an organic closed loop, with efficient data flow and logical coherence. For example, the ship impact frequency and temperature change data collected by the sensors are processed by the coupling analysis module, and the generated modal coupling coefficient directly drives the safety margin assessment. The assessment results then affect the adjustment of the early warning threshold in real time, ensuring that the early warning signal can accurately reflect the current risk status of the structure. This overcomes the shortcomings of traditional system modules being isolated and having a delayed response, and improves the overall integrity and timeliness of monitoring and early warning.

[0076] Traditional technical solutions have the following technical problems: existing sensor data acquisition systems suffer from problems such as limited sensor types, unreasonable deployment locations, and susceptibility to data transmission interference. This results in incomplete dimensions of the acquired physical parameters, insufficient spatial representativeness, and poor data integrity. For example, strain data of key stress nodes are missing, and vibration signals are distorted due to wireless transmission interference, affecting the accuracy of subsequent multi-factor analysis.

[0077] Based on this, the sensor data acquisition module includes a fiber optic strain sensing unit, a triaxial acceleration sensing unit, a temperature and humidity sensing unit, a water level sensing unit, and a data transmission unit. The outputs of the fiber optic strain sensing unit, the triaxial acceleration sensing unit, the temperature and humidity sensing unit, and the water level sensing unit are respectively connected to the input of the data transmission unit. The output of the data transmission unit is connected to the input of the multi-factor coupling analysis module. The data transmission unit uses industrial Ethernet for data transmission, supporting data encryption and breakpoint resume functionality. This solution constructs a comprehensive data acquisition system through the targeted deployment of multiple types of sensing units and wired transmission assurance. The fiber optic strain sensing unit focuses on strain monitoring of key stress nodes, the triaxial acceleration sensing unit captures the dynamic response of vibration-sensitive parts, and the temperature, humidity, and water level sensing units record environmental parameters. Data from each unit is transmitted via industrial Ethernet to ensure real-time performance and security. The technological benefits are reflected in the significant improvement in the comprehensiveness, accuracy, and reliability of the collected data. For example, the fiber optic grating sensing unit can accurately capture minute strain changes in key parts such as the dock beams, the triaxial accelerometer can record the impact vibration characteristics of ships when they berth, the wired transmission of industrial Ethernet avoids data loss caused by wireless interference, and the encryption and breakpoint resume functions ensure data integrity. This provides high-quality input for multi-factor coupled analysis, overcomes the shortcomings of traditional acquisition systems such as single parameters and data distortion, and lays the foundation for accurate safety assessment.

[0078] Traditional technical solutions have the following technical problems: the existing multi-factor analysis module and early warning threshold generation module have a crude internal structure, the parameter extraction and coupling calculation processes are separated, and the threshold adjustment is not effectively associated with the static parameters of the BIM model, resulting in redundant module functions and low data processing efficiency. For example, the coupling coefficient calculation is biased due to incomplete parameter extraction, and the threshold generation lacks the support of static design benchmarks.

[0079] Based on this, the multi-factor coupling analysis module includes a parameter extraction unit and a coupling coefficient calculation unit. The input of the parameter extraction unit is connected to the output of the sensor data acquisition module, and is used to extract parameters such as ship impact force frequency, structural natural frequency, and temperature change from the acquired physical parameters. The input of the coupling coefficient calculation unit is connected to the output of the parameter extraction unit, and is used to calculate the modal coupling coefficient based on the extracted parameters. The adaptive early warning threshold generation module includes a threshold benchmark acquisition unit and a threshold adjustment unit. The threshold benchmark acquisition unit is connected to the BIM model construction module, and is used to acquire the static design threshold from the BIM model. The threshold adjustment unit is connected to both the threshold benchmark acquisition unit and the multi-factor coupling analysis module, and is used to adjust the static design threshold in conjunction with the modal coupling coefficient to generate an adaptive early warning threshold.

[0080] This solution achieves a professional division of labor between parameter processing and threshold generation by refining the internal unit structure of the modules. The parameter extraction unit focuses on screening key parameters from the raw data to provide accurate input for coupling coefficient calculation; the coupling coefficient calculation unit completes multi-factor quantification based on a preset model; the threshold benchmark acquisition unit extracts static design data from the BIM model to provide a benchmark for threshold adjustment; and the threshold adjustment unit integrates dynamic coupling effects and static benchmarks to generate an appropriate threshold.

[0081] The technological benefits are reflected in more efficient data processing within the modules. For example, the parameter extraction unit can selectively filter noisy data to ensure the accuracy of key parameters such as ship impact force frequency. The coupling coefficient calculation unit outputs reliable results based on clean data. The threshold adjustment unit combines the static design threshold and dynamic coupling coefficient of the BIM model to generate adaptive thresholds that conform to design specifications and fit real-time operating conditions. This overcomes the shortcomings of low processing efficiency and poor reliability of results caused by the roughness of traditional module structures, and improves the overall accuracy and stability of the system.

[0082] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A BIM-based method for structural safety monitoring and early warning of wharf berths, characterized in that, include: S1: Construct a BIM model of the wharf berth structure and collect physical parameters of the structure during operation through a sensor network; S2: Perform multi-factor coupling analysis on the collected physical parameters to determine the structural modal coupling effect; S3: Calculate the dynamic safety margin of the structure based on the modal coupling effect, and generate an adaptive early warning threshold based on the dynamic safety margin; S4: Issue a warning signal when the monitored structural parameters exceed the adaptive warning threshold; The multi-factor coupling analysis includes obtaining the ship impact force frequency, structural natural frequency, temperature change, reference temperature, water level density change, freshwater density benchmark, creep stress increment and material yield strength. The above parameters are substituted into a preset calculation model to obtain the modal coupling coefficient. The modal coupling coefficient is used to quantify the synergistic effect between ship impact, tidal load and temperature gradient. The dynamic safety margin calculation includes obtaining the structural ultimate strength, equivalent stress, environmental acceleration factor, plastic strain rate and corrosion stress increment, and combining the modal coupling coefficient to obtain the dynamic safety margin index through integral calculation. The dynamic safety margin index changes dynamically over time to reflect the hidden degradation path of the structure. The modal coupling coefficient is calculated using the following formula: ; in, The modal coupling coefficient; , The weighting coefficients were determined through orthogonal experiments. This refers to the change in temperature. For reference temperature; The frequency of the ship's impact force; The natural frequency of the structure; This refers to changes in water level density. Used as a freshwater density benchmark; This represents the creep stress increment; This represents the yield strength of the material.

2. The method for monitoring and early warning of the structural safety of a wharf berth based on BIM according to claim 1, characterized in that, The sensor network includes fiber optic strain sensors, triaxial accelerometers, temperature and humidity sensors, and water level sensors. The fiber optic strain sensors are deployed at the stress nodes of the wharf berth structure to collect strain data. The triaxial accelerometers are installed at the vibration points of the structure to collect vibration acceleration data. The temperature and humidity sensors are deployed on the surface of the structure and in the surrounding environment to collect temperature and humidity data. The water level sensors are set in the water-adjacent area of ​​the wharf berth to collect water level data. All data collected by the sensors are uploaded to the data processing center in real time via wired transmission.

3. The method for monitoring and early warning of the structural safety of a wharf berth based on BIM according to claim 1, characterized in that, The dynamic safety margin index is calculated using the following formula: ； in, For dynamic safety margin index, The ultimate strength of the structure; Equivalent stress; This is the environmental acceleration factor, which is related to humidity and chloride ion concentration. Plastic strain rate; This represents the increase in corrosion stress. The modal coupling coefficient; For time.

4. The method for monitoring and early warning of the structural safety of a wharf berth based on BIM according to claim 1, characterized in that, The adaptive early warning threshold is generated using the following formula: ; in, For adaptive early warning threshold, For static design thresholds; This is the coupling sensitivity coefficient; The modal coupling coefficient; This is an increase in load beyond the design capacity; For design loads; This represents the vibration frequency offset. This is the critical resonance frequency.

5. A BIM-based safety monitoring and early warning system for wharf berth structures, applied to the BIM-based safety monitoring and early warning method for wharf berth structures as described in any one of claims 1-4, comprising a BIM model building module and a sensor data acquisition module, wherein the BIM model building module is used to build a three-dimensional BIM model of the wharf berth structure, and the sensor data acquisition module is used to collect physical parameters during the structure's operation, characterized in that... It also includes a multi-factor coupling analysis module, a dynamic safety margin assessment module, an adaptive early warning threshold generation module, and an early warning module; the multi-factor coupling analysis module is connected to the sensor data acquisition module and is used to receive physical parameters and perform multi-factor coupling analysis to determine the structural modal coupling effect. The dynamic safety margin assessment module is connected to the multi-factor coupling analysis module and is used to calculate the dynamic safety margin of the structure based on the modal coupling effect. The adaptive early warning threshold generation module is connected to the dynamic safety margin assessment module and the BIM model construction module, and is used to generate an adaptive early warning threshold based on the dynamic safety margin. The early warning module is connected to the adaptive early warning threshold generation module and is used to issue an early warning signal when the monitored structural parameters exceed the adaptive early warning threshold.

6. The BIM-based safety monitoring and early warning system for wharf berth structures according to claim 5, characterized in that, The sensor data acquisition module includes a fiber optic strain sensing unit, a triaxial acceleration sensing unit, a temperature and humidity sensing unit, a water level sensing unit, and a data transmission unit. The outputs of the fiber optic strain sensing unit, the triaxial acceleration sensing unit, the temperature and humidity sensing unit, and the water level sensing unit are respectively connected to the input of the data transmission unit. The output of the data transmission unit is connected to the input of the multi-factor coupling analysis module. The data transmission unit uses industrial Ethernet for data transmission and supports data encryption and breakpoint resume functionality.

7. A BIM-based safety monitoring and early warning system for wharf berth structures according to claim 5, characterized in that, The multi-factor coupling analysis module includes a parameter extraction unit and a coupling coefficient calculation unit; the input end of the parameter extraction unit is connected to the output end of the sensor data acquisition module, and is used to extract the ship impact force frequency, structural natural frequency, and temperature change from the acquired physical parameters; The input end of the coupling coefficient calculation unit is connected to the output end of the parameter extraction unit, and is used to calculate the modal coupling coefficient based on the extracted parameters; the adaptive early warning threshold generation module includes a threshold benchmark acquisition unit and a threshold adjustment unit; the threshold benchmark acquisition unit is connected to the BIM model construction module, and is used to obtain the static design threshold from the BIM model; The threshold adjustment unit, the threshold benchmark acquisition unit, and the multi-factor coupling analysis module are connected and used to adjust the static design threshold in combination with the modal coupling coefficient to generate an adaptive early warning threshold.

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